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阿尔茨海默病数字生物标志物多维景观和人工智能模型范围审查

Wenhao Qi¹, Xiaohong Zhu¹, Bin Wang¹

¹School of Nursing, Hangzhou Normal University, Hangzhou, China.

NPJ digital medicine

|June 16, 2025

概括

数字生物标志物显示出阿尔茨海默病 (AD) 诊断的前景,人工智能 (AI) 模型表现良好. 然而,外部验证和模型校准缺乏广泛的临床应用.

科学领域:

神经科学和计算科学的神经科学和计算科学
图书计量和人工智能图书计量和人工智能
生物标志物的发现和验证.

背景情况:

数字生物标志物对于阿尔茨海默病 (AD) 诊断越来越重要.
了解人工智能在阿尔茨海默氏症数字生物标志物研究中的现状至关重要.
现有的研究突出显示,需要对人工智能模型进行强有力的验证.

研究的目的:

进行对AD数字生物标志物研究的文献分析.
执行用于AD诊断的人工智能 (AI) 模型的范围审查.
确定该领域的关键研究趋势,资金和合作.

主要方法:

在5个主要数据库 (Web of Science,PubMed,Embase,IEEE Xplore,CINAHL) 中对431项研究进行了文献计量分析.
对86个人工智能 (AI) 模型的范围审查,这些模型应用于AD的数字生物标志物.
分析研究重点领域,资金,机构贡献和地理分布.

主要成果:

研究涉及来自44个国家的1403个机构的2571名研究人员,得到了224个赠款的支持.
关键的数字生物标志物领域包括运动活动,神经认知测试,眼睛跟踪和语音分析.
对AD诊断 (AUC 0.887) 和轻度认知障碍 (AUC 0.821) 的AI模型显示出潜力,但外部验证 (2项研究) 和校准 (3项研究) 严重不足.

结论:

针对AD的数字生物标志物的领域正在迅速发展,有显著的人工智能模型开发.
经典的机器学习模型普遍存在,但报告标准和性能验证需要改进.
将数字生物标志物纳入临床实践需要解决外部验证和模型校准的挑战.

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